Machine Learning Basics

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Machine Learning Basics Lecture slides for Chapter 5 of Deep Learning Ian Goodfellow 2016-09-26. TER 5. Machine Learning Basics . Linear Regression Linear regression example Optimization of w 3 2 1. MSE(train). 0. y 1. 2 3 x1 w1. Figure e : A linear regression problem, with a training set consisting of ten data p containing one feature. Because there is only one feature, the weight vect ns only a single parameter to learn, w . (Left)Observe that linear regression l (Goodfellow 2016). more parameters than training examples. We have little chance of ch Underfitting and Overfitting in tion that generalizes well when so many wildly di erent solutions ex xample, the quadratic model is perfectly matched to the true struct Polynomial Estimation sk so it generalizes well to new data. Underfitting Appropriate capacity Overfitting y y y x0 x0 x0. Figure : We fit three models to this example training set. The training da (Goodfellow 2016).

Basics Lecture slides for Chapter 5 of Deep Learning www.deeplearningbook.org Ian Goodfellow 2016-09-26 (Goodfellow 2016) Linear Regression CHAPTER 5. MACHINE LEARNING BASICS ... So far we have discussed the properties of various estimators for a training set of >. > ...

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